A Versatile Sn‐Substituted Argyrodite Sulfide Electrolyte for All‐Solid‐State Li Metal Batteries
Bibliographic record
Abstract
Abstract Sulfide‐based solid‐state electrolytes (SSEs) for all‐solid‐state Li metal batteries (ASSLMBs) are attracting significant attention due to their high ionic conductivity, inherently soft properties, and decent mechanical strength. However, the poor incompatibility with Li metal and air sensitivity have hindered their application. Herein, the Sn (IV) substitution for P (V) in argyrodite sulfide Li 6 PS 5 I (LPSI) SSEs is reported, in the preparation of novel LPSI‐ x Sn SSEs (where x is the Sn substitution percentage). Appropriate aliovalent element substitutions with larger atomic radius ( R > R ) provides the optimized LPSI‐20Sn electrolyte with a 125 times higher ionic conductivity compared to that of the LPSI electrolyte. The high ionic conductivity of LPSI‐20Sn enables the rich I‐containing electrolyte to serve as a stabilized interlayer against Li metal in sulfide‐based ASSLMBs with outstanding cycling stability and rate capability. Most importantly, benefiting from the strong Sn–S bonding in Sn‐substituted electrolytes, the LPSI‐20Sn electrolyte shows excellent structural stability and improved air stability after exposure to O 2 and moisture. The versatile Sn substitution in argyrodite LPSI electrolytes is believed to provide a new and effective strategy to achieve Li metal‐compatible and air‐stable sulfide‐based SSEs for large‐scale applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".